7 citations · 13 across the 21 of their papers we have counts for
22 papers
A provably convergent MM-GKS variant for large-scale inverse problems
Mirjeta Pasha, Eric de Sturler, Misha Kilmer
For high-quality images with sharp edges, a popular choice for edge-preserving regularization is using a general(ized) -norm of the gradient of the image. This can be imple…
Randomized Tucker-Sketched GMRES
Alberto Bucci, Martina Iannacito, Mirjeta Pasha +1
We address the problem of solving large-scale tensor-structured linear systems in the Tucker format. In this setting, standard iterative solvers such as GMRES face a fundamental bo…
A Sketched Generalized Krylov Subspace Method for Large-Scale Regularization
Davide Palitta, Mirjeta Pasha
The generalized Krylov subspace (GKS) method is an effective projection technique for large-scale Tikhonov regularization with a general regularization matrix. As the subspace expa…
Nonlinear RMM-GKS for Large-Scale Dynamic and Streaming Inverse Problems with Uncertain Forward Operators
Toluwani Okunola, Mirjeta Pasha, Misha E. Kilmer +2
Many practical imaging systems suffer from uncertainty in acquisition geometry -- such as projection angles in computed tomography or sensor positions in photoacoustic tomography -…
An Efficient Cumulative Edge-Detection Method for Image Reconstruction
Toluwani Okunola, Mirjeta Pasha, Misha E. Kilmer
When reconstructing images from noisy measurements, such as in medical scans or scientific imaging, we face an inverse problem: recovering an unknown image from indirect, corrupted…
Efficient Sketching-Based Summation of Tucker Tensors
Rudi Smith, Mirjeta Pasha, Andrés Galindo-Olarte +5
We present efficient, sketching-based methods for the summation of tensors in Tucker format. Leveraging the algebraic structure of Khatri-Rao and Kronecker products, our approach e…
Hybrid ABBA-GMRES for Unmatched Backprojectors in Large Scale X-Ray Computerized Tomography
Ryan Bentley, Mirjeta Pasha, Malena Sabaté Landman +2
In large-scale X-ray computed tomography (CT), matrix-free iterative methods are essential due to the prohibitive cost of explicitly forming the system matrix. In practice, forward…
A Scalable Sequential Framework for Dynamic Inverse Problems via Model Parameter Estimation
Aryeh Keating, Mirjeta Pasha
Large-scale dynamic inverse problems are often ill-posed due to model complexity and the high dimensionality of the unknown parameters. Regularization is commonly employed to mitig…
A Nodal Discontinuous Galerkin Method with Rank-Adaptive Velocity Space Representation for the Multiscale BGK Model
Andres Galindo-Olarte, Joseph Nakao, Mirjeta Pasha +2
A novel hybrid algorithm is presented for the Boltzmann-BGK equation, in which a rank-adaptive decomposition is applied solely in the velocity subspace, while a full-rank represent…
Priorconditioned Sparsity-Promoting Projection Methods for Deterministic and Bayesian Linear Inverse Problems
Jonathan Lindbloom, Mirjeta Pasha, Jan Glaubitz +1
High-quality reconstructions of signals and images with sharp edges are needed in a wide range of applications. To overcome the large dimensionality of the parameter space and the…
Efficient Dynamic Image Reconstruction with motion estimation
Toluwani Okunola, Mirjeta Pasha, Misha Kilmer +1
Dynamic inverse problems are challenging to solve due to the need to identify and incorporate appropriate regularization in both space and time. Moreover, the very large scale natu…
TRIPs-Py: Techniques for Regularization of Inverse Problems in Python
Mirjeta Pasha, Silvia Gazzola, Connor Sanderford +1
In this paper, we describe TRIPs-Py, a new Python package of linear discrete inverse problems solvers and test problems. The goal of the package is two-fold: 1) to provide tools fo…
Tensor Completion with BMD Factor Nuclear Norm Minimization
Fan Tian, Mirjeta Pasha, Misha E. Kilmer +2
This paper is concerned with the problem of recovering third-order tensor data from limited samples. A recently proposed tensor decomposition (BMD) method has been shown to efficie…
Recycling MMGKS for large-scale dynamic and streaming data
Mirjeta Pasha, Eric de Sturler, Misha E. Kilmer
Reconstructing high-quality images with sharp edges requires the use of edge-preserving constraints in the regularized form of the inverse problem. The use of the -norm on…
Spatiotemporal Besov Priors for Bayesian Inverse Problems
Shiwei Lan, Mirjeta Pasha, Shuyi Li +1
Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast. Many inverse…
Bayesian Spatiotemporal Modeling for Inverse Problems
Shiwei Lan, Shuyi Li, Mirjeta Pasha
Inverse problems with spatiotemporal observations are ubiquitous in scientific studies and engineering applications. In these spatiotemporal inverse problems, observed multivariate…
The Image Deblurring Problem: Matrices, Wavelets, and Multilevel Methods
David Austin, Malena I. Español, Mirjeta Pasha
The image deblurring problem consists of reconstructing images from blur and noise contaminated available data. In this AMS Notices article, we provide an overview of some well kno…
Randomized algorithms for rounding in the Tensor-Train format
Hussam Al Daas, Grey Ballard, Paul Cazeaux +5
The Tensor-Train (TT) format is a highly compact low-rank representation for high-dimensional tensors. TT is particularly useful when representing approximations to the solutions o…
Efficient learning methods for large-scale optimal inversion design
Julianne Chung, Matthias Chung, Silvia Gazzola +1
In this work, we investigate various approaches that use learning from training data to solve inverse problems, following a bi-level learning approach. We consider a general framew…
Efficient edge-preserving methods for dynamic inverse problems
Mirjeta Pasha, Arvind K. Saibaba, Silvia Gazzola +2
We consider efficient methods for computing solutions to dynamic inverse problems, where both the quantities of interest and the forward operator (measurement process) may change a…
An Variable Projection Method for Large-Scale Separable Nonlinear Inverse Problems
Malena Espanol, Mirjeta Pasha
The variable projection (VarPro) method is an efficient method to solve separable nonlinear least squares problems. In this paper, we propose a modified VarPro for large-scale sepa…
Optimal Transport for Parameter Identification of Chaotic Dynamics via Invariant Measures
Yunan Yang, Levon Nurbekyan, Elisa Negrini +2
We study an optimal transportation approach for recovering parameters in dynamical systems with a single smoothly varying attractor. We assume that the data is not sufficient for e…